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[Feature] PySRSequenceRegressor #94
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Hello, Could this be currently implemented? If yes can you specify how the preprocessing would look like (I might be interested to try this on data I currently work on). |
Hello, This sounds really great for time series data, but as falknerdominik said, how would the preprocessing work? |
It should be fairly straightforward to implement. Just need to find some time to do it. I think it could wrap Since I've evidently been too busy to add this so far, let me know if one of you would be interested in working on it and I can point you in the right direction. |
I would be happy to work on this! |
@MilesCranmer can you assign this to me pls? |
@MilesCranmer I'm writing a research paper on symbolic regression for recurrence relations; can I pls use this in my paper? |
also is it or |
To define a symbolic regression model predict recurrent relations in sequences, I want to create a
PySRSequenceRegressor
(see #88 for scikit-learn API) that will automatically set this up for the user given a sequenceX
.This would require basically no core modifications. It's just a preprocessing step. This could also be used to model any sort of fixed step size time series data, including differential equations (although the learned symbolic model would just be single-step prediction; not rollouts), so I think this would be a nice addition.
The required arguments for this would be:
history_length
which tells the data preprocessing how many historical features to allocate a single datapoint for it to predict the next step.The user would be allowed to pass a 1D array (single sequence) or a 3D array (batch of sequences with multiple features each). This will not allow for 2D arrays as input since this is ambiguous with regard to batching vs multi-feature. Passing a 2D array will raise an error telling the user to pass a 3D array, and give the expected axis configuration.
A 3D array as input will raise a warning letting the user know which axis is being interpreted as batch/feature. This warning could be silenced with a flag.
The user could also use
PySRRegressor
and do the preprocessing themselves.(cc @patrick-kidger @kazewong, in case of interest)
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